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Ramesh K. Agarwal

Washington University in St. Louis

Professor Ramesh Agarwal is the William Palm Professor of Engineering in the Department of Mechanical Engineering and Materials Science at Washington University in St. Louis, USA. He received a Ph.D. in Aeronautical Sciences from Stanford University in 1975. Over a period of 40 years, Professor Agarwal has worked in various areas of Computational Science and Engineering - Computational Fluid Dynamics (CFD), Computational Acoustics and Electromagnetics, and Multidisciplinary Design and Optimization. He has developed computational codes that have been widely used for the aerodynamic analysis and design of aircraft and spacecraft including their propulsion systems. In recent years, he has been working in the area of sustainable air and ground transportation and has been the co-editor of Wiley Encyclopedia on Green Aviation.

4books edited

5chapters authored

Latest work with IntechOpen by Ramesh K. Agarwal

The book describes the state of the art and latest advancements in technologies for various areas of aircraft systems. In particular it covers wide variety of topics in aircraft structures and advanced materials, control systems, electrical systems, inspection and maintenance, avionics and radar and some miscellaneous topics such as green aviation. The authors are leading experts in their fields. Both the researchers and the students should find the material useful in their work.

Efficient carbon capture and storage (CCS) technologies are needed to address the rising carbon emissions from power generation using fossil fuels that have been linked to global warming and climate change. Chemical looping combustion (CLC) is one such technology that has shown great promise due to its potential for high-purity carbon capture at low cost. Another CCS technology that has garnered interest in recent years is calcium looping (CaL), which utilizes calcium oxide and the carbonation-calcination equilibrium reactions to capture CO2 from the flue stream of fossil fuel power plants. Computational fluid dynamics (CFD) simulations of two CLC reactors are presented in this chapter, along with system level simulations of CaL for postcombustion carbon capture. CFD simulation of a CLC reactor based on a dual fluidized bed reactor is developed using the Eulerian approach to characterize the chemical reactions in the system. The solid phase consists of a Fe-based oxygen carrier while the gaseous fuel used is syngas. Later, the detailed hydrodynamics in a CLC system designed for solid coal fuel is presented based on a cold flow experimental setup at National Energy Technology Laboratory using the Lagrangian particle-tracking method. The process simulation of CaL using Aspen Plus shows an increasing marginal energy penalty associated with an increase in the CO2 capture efficiency, which suggests a limit on the maximum carbon capture efficiency in practical applications of CaL before the energy penalty becomes too large.

The focus of this chapter is on the shape optimization of the Busemann-type biplane airfoil for drag reduction under both non-lifting and lifting conditions using genetic algorithms. The concept of biplane airfoil was first introduced by Adolf Busemann in 1935. Under design conditions at a specific supersonic flow speed, the Busemann biplane airfoil eliminates all wave drag due to its symmetrical biplane configuration; however, it produces zero lift. Previous research has shown that the original Busemann biplane airfoil shows a poor performance under off-design conditions. In order to address this problem of zero lift and to improve the off-design-condition performance, shape optimization of an asymmetric biplane airfoil is performed. The commercially available computational fluid dynamics (CFD) solver ANSYS FLUENT is employed for computing the inviscid supersonic flow past the biplane airfoil. A single-objective genetic algorithm (SOGA) is employed for shape optimization under the non-lifting condition to minimize the drag, and a multi-objective genetic algorithm (MOGA) is used for shape optimization under the lifting condition to maximize both the lift and the lift-to-drag ratio. The results obtained from both SOGA and MOGA show a significant improvement in the design and off-design-condition performance of the optimized Busemann biplane airfoil compared to the original airfoil.